Extracting Parsimonious Quantitative Predictors of Biological Effectiveness from ‘First-Principles’ Radiobiology: Application to the Mixed-Quality Problem
Abstract
Purpose
Advances in ‘first-principles’ models of radiation response allow, in principle, for accurate mechanism-aware prediction of how biological effectiveness and effective linear quadratic (LQ) parameters change across various experimental and clinical conditions. In practice, however, first-principles models come with an overabundance of parameters, the majority of which are practically unidentifiable and, moreover, likely unnecessary if one simply wishes to predict the effect when changing a specific modifier of interest. In this study, we demonstrate the potential of model reduction, starting from a detailed mechanistic description, as a systematic strategy for deriving parsimonious, experimentally falsifiable radiobiological descriptors.
Methods
The manifold boundary approximation method (MBAM) is a method for systematically controlling hierarchies of approximations that reduced detailed many-parameter models into simpler models, based on information geometric approaches to global sensitivity analysis. We apply MBAM to a Mechanistic Model of DNA Repair and Survival (MEDRAS), for the problem of cell survival prediction following an acute exposure. The full MEDRAS model for an arbitrary mixed-quality exposure is found to be structurally equivalent to a reduced three-parameter model for an effective uniform-quality, named MEDRAS-LPL. Additional analysis of MEDRAS-LPL identifies two distinct limiting regimes in parameter space, corresponding to sparsely ionizing and densely ionizing radiation.
Results
Mapping of MEDRAS-LPL parameter space on to effective LQ space demonstrates that parameters close to the sparsely ionizing boundary line up with expectations from the theory of dual radiation, while parameters close to the densely ionizing boundary line up with expectations from a purely linear model based on a target-theory description. Moreover, our formalism predicts enhanced synergistic interactions between sparsely ionizing and densely ionizing radiation beyond the Zaider-Rossi model (ZRM).
Conclusion
Our results serve as a demonstration of the potential of reduced-order mechanistic radiobiological modeling to generate novel hypotheses that can inform future experimental designs and optimization strategies in radiobiology.